Abstract
Recent research has considered the role of locality in GP representations. We use a modified statistical technique drawn from numerical ecology, the Mantel test, to measure the locality of integer-encoded GP. Weak locality is identified in a case study on Cartesian Genetic Programming (CGP), a directed acyclic graph representation. A method of varying syntactic program locality continuously through the application of a biased mutation operator is demonstrated. The impact of varying locality under the new measure is assessed over a randomly generated set of polynomial symbolic regression problems. We observe that enforcing higher levels of locality in CGP is associated with poorer performance on the problem set and discuss implications in the context of existing models of GP genotype-phenotype maps.
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Seaton, T., Miller, J.F., Clarke, T. (2012). An Ecological Approach to Measuring Locality in Linear Genotype to Phenotype Maps. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds) Genetic Programming. EuroGP 2012. Lecture Notes in Computer Science, vol 7244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29139-5_15
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DOI: https://doi.org/10.1007/978-3-642-29139-5_15
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